Overview

Dataset statistics

Number of variables13
Number of observations865
Missing cells305
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory94.6 KiB
Average record size in memory112.0 B

Variable types

Numeric6
Categorical5
Boolean2

Alerts

id is highly overall correlated with dataset and 1 other fieldsHigh correlation
dataset is highly overall correlated with idHigh correlation
cp is highly overall correlated with numHigh correlation
num is highly overall correlated with id and 1 other fieldsHigh correlation
chol has 188 (21.7%) missing valuesMissing
fbs has 89 (10.3%) missing valuesMissing
oldpeak has 19 (2.2%) missing valuesMissing
id has unique valuesUnique
oldpeak has 370 (42.8%) zerosZeros

Reproduction

Analysis started2023-08-29 19:37:34.575715
Analysis finished2023-08-29 19:37:43.412087
Duration8.84 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct865
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.26705
Minimum1
Maximum920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-08-29T15:37:43.580090image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.2
Q1217
median433
Q3650
95-th percentile855.8
Maximum920
Range919
Interquartile range (IQR)433

Descriptive statistics

Standard deviation256.58632
Coefficient of variation (CV)0.58679545
Kurtosis-1.1200026
Mean437.26705
Median Absolute Deviation (MAD)217
Skewness0.07469906
Sum378236
Variance65836.539
MonotonicityStrictly increasing
2023-08-29T15:37:43.804223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
582 1
 
0.1%
571 1
 
0.1%
572 1
 
0.1%
573 1
 
0.1%
574 1
 
0.1%
575 1
 
0.1%
576 1
 
0.1%
577 1
 
0.1%
578 1
 
0.1%
Other values (855) 855
98.8%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
920 1
0.1%
918 1
0.1%
916 1
0.1%
915 1
0.1%
914 1
0.1%
913 1
0.1%
912 1
0.1%
911 1
0.1%
910 1
0.1%
909 1
0.1%

age
Real number (ℝ)

Distinct50
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.150289
Minimum28
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-08-29T15:37:44.019059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile37
Q146
median54
Q360
95-th percentile67.8
Maximum77
Range49
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.3743186
Coefficient of variation (CV)0.1763738
Kurtosis-0.38881244
Mean53.150289
Median Absolute Deviation (MAD)6
Skewness-0.17282552
Sum45975
Variance87.87785
MonotonicityNot monotonic
2023-08-29T15:37:44.236826image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 48
 
5.5%
58 40
 
4.6%
57 37
 
4.3%
55 37
 
4.3%
52 36
 
4.2%
59 35
 
4.0%
56 35
 
4.0%
53 33
 
3.8%
62 31
 
3.6%
48 30
 
3.5%
Other values (40) 503
58.2%
ValueCountFrequency (%)
28 1
 
0.1%
29 3
 
0.3%
30 1
 
0.1%
31 2
 
0.2%
32 5
0.6%
33 2
 
0.2%
34 7
0.8%
35 9
1.0%
36 6
0.7%
37 11
1.3%
ValueCountFrequency (%)
77 2
 
0.2%
76 2
 
0.2%
75 3
 
0.3%
74 5
0.6%
73 1
 
0.1%
72 3
 
0.3%
71 4
0.5%
70 7
0.8%
69 8
0.9%
68 9
1.0%

sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
Male
672 
Female
193 

Length

Max length6
Median length4
Mean length4.4462428
Min length4

Characters and Unicode

Total characters3846
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 672
77.7%
Female 193
 
22.3%

Length

2023-08-29T15:37:44.449535image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T15:37:44.659686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
male 672
77.7%
female 193
 
22.3%

Most occurring characters

ValueCountFrequency (%)
e 1058
27.5%
a 865
22.5%
l 865
22.5%
M 672
17.5%
F 193
 
5.0%
m 193
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2981
77.5%
Uppercase Letter 865
 
22.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1058
35.5%
a 865
29.0%
l 865
29.0%
m 193
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
M 672
77.7%
F 193
 
22.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3846
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1058
27.5%
a 865
22.5%
l 865
22.5%
M 672
17.5%
F 193
 
5.0%
m 193
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1058
27.5%
a 865
22.5%
l 865
22.5%
M 672
17.5%
F 193
 
5.0%
m 193
 
5.0%

dataset
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
Cleveland
304 
Hungary
293 
VA Long Beach
147 
Switzerland
121 

Length

Max length13
Median length11
Mean length9.2820809
Min length7

Characters and Unicode

Total characters8029
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCleveland
2nd rowCleveland
3rd rowCleveland
4th rowCleveland
5th rowCleveland

Common Values

ValueCountFrequency (%)
Cleveland 304
35.1%
Hungary 293
33.9%
VA Long Beach 147
17.0%
Switzerland 121
 
14.0%

Length

2023-08-29T15:37:44.826018image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T15:37:45.039272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
cleveland 304
26.2%
hungary 293
25.3%
va 147
12.7%
long 147
12.7%
beach 147
12.7%
switzerland 121
 
10.4%

Most occurring characters

ValueCountFrequency (%)
e 876
 
10.9%
a 865
 
10.8%
n 865
 
10.8%
l 729
 
9.1%
g 440
 
5.5%
d 425
 
5.3%
r 414
 
5.2%
C 304
 
3.8%
v 304
 
3.8%
294
 
3.7%
Other values (15) 2513
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6429
80.1%
Uppercase Letter 1306
 
16.3%
Space Separator 294
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 876
13.6%
a 865
13.5%
n 865
13.5%
l 729
11.3%
g 440
6.8%
d 425
6.6%
r 414
6.4%
v 304
 
4.7%
u 293
 
4.6%
y 293
 
4.6%
Other values (7) 925
14.4%
Uppercase Letter
ValueCountFrequency (%)
C 304
23.3%
H 293
22.4%
B 147
11.3%
V 147
11.3%
L 147
11.3%
A 147
11.3%
S 121
 
9.3%
Space Separator
ValueCountFrequency (%)
294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7735
96.3%
Common 294
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 876
 
11.3%
a 865
 
11.2%
n 865
 
11.2%
l 729
 
9.4%
g 440
 
5.7%
d 425
 
5.5%
r 414
 
5.4%
C 304
 
3.9%
v 304
 
3.9%
H 293
 
3.8%
Other values (14) 2220
28.7%
Common
ValueCountFrequency (%)
294
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 876
 
10.9%
a 865
 
10.8%
n 865
 
10.8%
l 729
 
9.1%
g 440
 
5.5%
d 425
 
5.3%
r 414
 
5.2%
C 304
 
3.8%
v 304
 
3.8%
294
 
3.7%
Other values (15) 2513
31.3%

cp
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
asymptomatic
469 
non-anginal
186 
atypical angina
168 
typical angina
 
42

Length

Max length15
Median length12
Mean length12.46474
Min length11

Characters and Unicode

Total characters10782
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtypical angina
2nd rowasymptomatic
3rd rowasymptomatic
4th rownon-anginal
5th rowatypical angina

Common Values

ValueCountFrequency (%)
asymptomatic 469
54.2%
non-anginal 186
 
21.5%
atypical angina 168
 
19.4%
typical angina 42
 
4.9%

Length

2023-08-29T15:37:45.235519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T15:37:45.453036image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
asymptomatic 469
43.6%
angina 210
19.5%
non-anginal 186
 
17.3%
atypical 168
 
15.6%
typical 42
 
3.9%

Most occurring characters

ValueCountFrequency (%)
a 2108
19.6%
n 1164
10.8%
t 1148
10.6%
i 1075
10.0%
m 938
8.7%
y 679
 
6.3%
p 679
 
6.3%
c 679
 
6.3%
o 655
 
6.1%
s 469
 
4.3%
Other values (4) 1188
11.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10386
96.3%
Space Separator 210
 
1.9%
Dash Punctuation 186
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2108
20.3%
n 1164
11.2%
t 1148
11.1%
i 1075
10.4%
m 938
9.0%
y 679
 
6.5%
p 679
 
6.5%
c 679
 
6.5%
o 655
 
6.3%
s 469
 
4.5%
Other values (2) 792
 
7.6%
Space Separator
ValueCountFrequency (%)
210
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10386
96.3%
Common 396
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2108
20.3%
n 1164
11.2%
t 1148
11.1%
i 1075
10.4%
m 938
9.0%
y 679
 
6.5%
p 679
 
6.5%
c 679
 
6.5%
o 655
 
6.3%
s 469
 
4.5%
Other values (2) 792
 
7.6%
Common
ValueCountFrequency (%)
210
53.0%
- 186
47.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2108
19.6%
n 1164
10.8%
t 1148
10.6%
i 1075
10.0%
m 938
8.7%
y 679
 
6.3%
p 679
 
6.3%
c 679
 
6.3%
o 655
 
6.1%
s 469
 
4.3%
Other values (4) 1188
11.0%

trestbps
Real number (ℝ)

Distinct60
Distinct (%)7.0%
Missing5
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean132.28605
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-08-29T15:37:45.653939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile105.95
Q1120
median130
Q3140
95-th percentile160.2
Maximum200
Range120
Interquartile range (IQR)20

Descriptive statistics

Standard deviation18.536175
Coefficient of variation (CV)0.14012192
Kurtosis0.63057795
Mean132.28605
Median Absolute Deviation (MAD)10
Skewness0.63015892
Sum113766
Variance343.58979
MonotonicityNot monotonic
2023-08-29T15:37:45.886780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 131
15.1%
130 115
13.3%
140 102
 
11.8%
110 59
 
6.8%
150 56
 
6.5%
160 50
 
5.8%
125 29
 
3.4%
115 19
 
2.2%
135 18
 
2.1%
128 17
 
2.0%
Other values (50) 264
30.5%
ValueCountFrequency (%)
80 1
 
0.1%
92 1
 
0.1%
94 2
 
0.2%
95 6
 
0.7%
96 1
 
0.1%
98 1
 
0.1%
100 15
1.7%
101 1
 
0.1%
102 3
 
0.3%
104 3
 
0.3%
ValueCountFrequency (%)
200 4
 
0.5%
192 1
 
0.1%
190 2
 
0.2%
185 1
 
0.1%
180 12
1.4%
178 3
 
0.3%
174 1
 
0.1%
172 2
 
0.2%
170 14
1.6%
165 2
 
0.2%

chol
Real number (ℝ)

MISSING 

Distinct209
Distinct (%)30.9%
Missing188
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean247.12851
Minimum85
Maximum603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-08-29T15:37:46.114032image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile169.8
Q1210
median240
Q3276
95-th percentile340.2
Maximum603
Range518
Interquartile range (IQR)66

Descriptive statistics

Standard deviation58.646265
Coefficient of variation (CV)0.2373108
Kurtosis5.0086258
Mean247.12851
Median Absolute Deviation (MAD)33
Skewness1.3597063
Sum167306
Variance3439.3843
MonotonicityNot monotonic
2023-08-29T15:37:46.349920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
254 10
 
1.2%
220 10
 
1.2%
223 9
 
1.0%
230 9
 
1.0%
219 9
 
1.0%
246 8
 
0.9%
260 8
 
0.9%
216 8
 
0.9%
240 8
 
0.9%
211 8
 
0.9%
Other values (199) 590
68.2%
(Missing) 188
 
21.7%
ValueCountFrequency (%)
85 1
0.1%
100 2
0.2%
117 1
0.1%
126 1
0.1%
129 1
0.1%
131 1
0.1%
132 1
0.1%
141 1
0.1%
147 2
0.2%
149 2
0.2%
ValueCountFrequency (%)
603 1
0.1%
564 1
0.1%
529 1
0.1%
518 1
0.1%
491 1
0.1%
468 1
0.1%
466 1
0.1%
458 1
0.1%
417 1
0.1%
412 1
0.1%

fbs
Boolean

MISSING 

Distinct2
Distinct (%)0.3%
Missing89
Missing (%)10.3%
Memory size13.5 KiB
False
659 
True
117 
(Missing)
89 
ValueCountFrequency (%)
False 659
76.2%
True 117
 
13.5%
(Missing) 89
 
10.3%
2023-08-29T15:37:46.565887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

restecg
Categorical

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size13.5 KiB
normal
531 
lv hypertrophy
181 
st-t abnormality
151 

Length

Max length16
Median length6
Mean length9.4275782
Min length6

Characters and Unicode

Total characters8136
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlv hypertrophy
2nd rowlv hypertrophy
3rd rowlv hypertrophy
4th rownormal
5th rowlv hypertrophy

Common Values

ValueCountFrequency (%)
normal 531
61.4%
lv hypertrophy 181
 
20.9%
st-t abnormality 151
 
17.5%
(Missing) 2
 
0.2%

Length

2023-08-29T15:37:46.723104image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T15:37:46.925097image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
normal 531
44.4%
lv 181
 
15.1%
hypertrophy 181
 
15.1%
st-t 151
 
12.6%
abnormality 151
 
12.6%

Most occurring characters

ValueCountFrequency (%)
r 1044
12.8%
l 863
10.6%
o 863
10.6%
a 833
10.2%
n 682
8.4%
m 682
8.4%
t 634
7.8%
y 513
 
6.3%
p 362
 
4.4%
h 362
 
4.4%
Other values (7) 1298
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7653
94.1%
Space Separator 332
 
4.1%
Dash Punctuation 151
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1044
13.6%
l 863
11.3%
o 863
11.3%
a 833
10.9%
n 682
8.9%
m 682
8.9%
t 634
8.3%
y 513
6.7%
p 362
 
4.7%
h 362
 
4.7%
Other values (5) 815
10.6%
Space Separator
ValueCountFrequency (%)
332
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 151
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7653
94.1%
Common 483
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1044
13.6%
l 863
11.3%
o 863
11.3%
a 833
10.9%
n 682
8.9%
m 682
8.9%
t 634
8.3%
y 513
6.7%
p 362
 
4.7%
h 362
 
4.7%
Other values (5) 815
10.6%
Common
ValueCountFrequency (%)
332
68.7%
- 151
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1044
12.8%
l 863
10.6%
o 863
10.6%
a 833
10.2%
n 682
8.4%
m 682
8.4%
t 634
7.8%
y 513
 
6.3%
p 362
 
4.4%
h 362
 
4.4%
Other values (7) 1298
16.0%

thalch
Real number (ℝ)

Distinct119
Distinct (%)13.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean137.5544
Minimum60
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-08-29T15:37:47.116968image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile95
Q1120
median140
Q3157
95-th percentile178
Maximum202
Range142
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.94002
Coefficient of variation (CV)0.18858008
Kurtosis-0.4818714
Mean137.5544
Median Absolute Deviation (MAD)20
Skewness-0.21201179
Sum118847
Variance672.88464
MonotonicityNot monotonic
2023-08-29T15:37:47.332710image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 43
 
5.0%
140 41
 
4.7%
120 35
 
4.0%
130 29
 
3.4%
160 26
 
3.0%
110 21
 
2.4%
170 20
 
2.3%
125 20
 
2.3%
122 16
 
1.8%
142 14
 
1.6%
Other values (109) 599
69.2%
ValueCountFrequency (%)
60 1
0.1%
63 1
0.1%
67 1
0.1%
69 1
0.1%
70 1
0.1%
71 1
0.1%
72 2
0.2%
73 1
0.1%
77 1
0.1%
78 1
0.1%
ValueCountFrequency (%)
202 1
 
0.1%
195 1
 
0.1%
194 1
 
0.1%
192 1
 
0.1%
190 2
0.2%
188 2
0.2%
187 1
 
0.1%
186 2
0.2%
185 4
0.5%
184 4
0.5%

exang
Boolean

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size13.5 KiB
False
528 
True
336 
(Missing)
 
1
ValueCountFrequency (%)
False 528
61.0%
True 336
38.8%
(Missing) 1
 
0.1%
2023-08-29T15:37:47.562623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

oldpeak
Real number (ℝ)

MISSING  ZEROS 

Distinct43
Distinct (%)5.1%
Missing19
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean0.90626478
Minimum0
Maximum6.2
Zeros370
Zeros (%)42.8%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-08-29T15:37:47.989325image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31.5
95-th percentile3
Maximum6.2
Range6.2
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.0711918
Coefficient of variation (CV)1.1819855
Kurtosis1.0963943
Mean0.90626478
Median Absolute Deviation (MAD)0.5
Skewness1.1561648
Sum766.7
Variance1.1474518
MonotonicityNot monotonic
2023-08-29T15:37:48.190077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 370
42.8%
1 83
 
9.6%
2 76
 
8.8%
1.5 48
 
5.5%
3 28
 
3.2%
0.5 19
 
2.2%
1.2 17
 
2.0%
2.5 16
 
1.8%
1.4 15
 
1.7%
0.8 15
 
1.7%
Other values (33) 159
18.4%
(Missing) 19
 
2.2%
ValueCountFrequency (%)
0 370
42.8%
0.1 9
 
1.0%
0.2 14
 
1.6%
0.3 5
 
0.6%
0.4 10
 
1.2%
0.5 19
 
2.2%
0.6 14
 
1.6%
0.7 5
 
0.6%
0.8 15
 
1.7%
0.9 4
 
0.5%
ValueCountFrequency (%)
6.2 1
 
0.1%
5.6 1
 
0.1%
5 1
 
0.1%
4.4 1
 
0.1%
4.2 2
 
0.2%
4 8
0.9%
3.8 1
 
0.1%
3.7 1
 
0.1%
3.6 4
0.5%
3.5 2
 
0.2%

num
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
1
473 
0
392 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters865
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 473
54.7%
0 392
45.3%

Length

2023-08-29T15:37:48.387131image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T15:37:48.573262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 473
54.7%
0 392
45.3%

Most occurring characters

ValueCountFrequency (%)
1 473
54.7%
0 392
45.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 865
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 473
54.7%
0 392
45.3%

Most occurring scripts

ValueCountFrequency (%)
Common 865
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 473
54.7%
0 392
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 473
54.7%
0 392
45.3%

Interactions

2023-08-29T15:37:41.262320image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:35.542727image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:36.843943image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:37.921735image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:39.058757image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:40.120581image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:41.443445image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:35.714725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:37.011030image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:38.102455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:39.235472image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:40.303631image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:41.621481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:35.887767image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:37.186059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:38.288499image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:39.404892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:40.484632image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:42.009527image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:36.292091image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:37.374132image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:38.482049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:39.576262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:40.681708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:42.188557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:36.458812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:37.544175image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:38.662346image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:39.751570image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:40.858878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:42.393520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:36.662399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:37.739184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:38.866109image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:39.935583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-29T15:37:41.067863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-08-29T15:37:48.726167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idagetrestbpscholthalcholdpeaksexdatasetcpfbsrestecgexangnum
id1.0000.1960.060-0.012-0.4740.0730.3220.8930.2700.2550.4340.3560.599
age0.1961.0000.2600.101-0.3500.2980.0000.2530.1580.2070.1650.1900.283
trestbps0.0600.2601.0000.089-0.0890.1400.0000.0940.0560.1370.0520.1300.125
chol-0.0120.1010.0891.000-0.0330.0740.1280.0000.0000.0000.0780.0580.074
thalch-0.474-0.350-0.089-0.0331.000-0.2090.1690.2530.2210.0000.1160.3890.393
oldpeak0.0730.2980.1400.074-0.2091.0000.1150.1570.1850.0000.0720.4350.417
sex0.3220.0000.0000.1280.1690.1151.0000.2680.1960.0570.0270.1750.305
dataset0.8930.2530.0940.0000.2530.1570.2681.0000.2110.2530.4330.2480.421
cp0.2700.1580.0560.0000.2210.1850.1960.2111.0000.0350.0990.4450.548
fbs0.2550.2070.1370.0000.0000.0000.0570.2530.0351.0000.1300.0000.113
restecg0.4340.1650.0520.0780.1160.0720.0270.4330.0990.1301.0000.0790.100
exang0.3560.1900.1300.0580.3890.4350.1750.2480.4450.0000.0791.0000.460
num0.5990.2830.1250.0740.3930.4170.3050.4210.5480.1130.1000.4601.000

Missing values

2023-08-29T15:37:42.668576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-29T15:37:43.009716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-29T15:37:43.263800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idagesexdatasetcptrestbpscholfbsrestecgthalchexangoldpeaknum
0163MaleClevelandtypical angina145.0233.0Truelv hypertrophy150.0False2.30
1267MaleClevelandasymptomatic160.0286.0Falselv hypertrophy108.0True1.51
2367MaleClevelandasymptomatic120.0229.0Falselv hypertrophy129.0True2.61
3437MaleClevelandnon-anginal130.0250.0Falsenormal187.0False3.50
4541FemaleClevelandatypical angina130.0204.0Falselv hypertrophy172.0False1.40
5656MaleClevelandatypical angina120.0236.0Falsenormal178.0False0.80
6762FemaleClevelandasymptomatic140.0268.0Falselv hypertrophy160.0False3.61
7857FemaleClevelandasymptomatic120.0354.0Falsenormal163.0True0.60
8963MaleClevelandasymptomatic130.0254.0Falselv hypertrophy147.0False1.41
91053MaleClevelandasymptomatic140.0203.0Truelv hypertrophy155.0True3.11
idagesexdatasetcptrestbpscholfbsrestecgthalchexangoldpeaknum
90890974MaleVA Long Beachasymptomatic155.0310.0Falsenormal112.0True1.51
90991068MaleVA Long Beachnon-anginal134.0254.0Truenormal151.0True0.00
91091151FemaleVA Long Beachasymptomatic114.0258.0Truelv hypertrophy96.0False1.00
91191262MaleVA Long Beachasymptomatic160.0254.0Truest-t abnormality108.0True3.01
91291353MaleVA Long Beachasymptomatic144.0300.0Truest-t abnormality128.0True1.51
91391462MaleVA Long Beachasymptomatic158.0170.0Falsest-t abnormality138.0True0.01
91491546MaleVA Long Beachasymptomatic134.0310.0Falsenormal126.0False0.01
91591654FemaleVA Long Beachasymptomatic127.0333.0Truest-t abnormality154.0False0.01
91791855MaleVA Long Beachasymptomatic122.0223.0Truest-t abnormality100.0False0.01
91992062MaleVA Long Beachatypical angina120.0254.0Falselv hypertrophy93.0True0.01